Predictive cheminformatics modeling of reorganization energy (RE) for p-type organic semiconductors: Integration of quantitative read-across structure-property relationship (q-RASPR) and stacking regression analysis

Organic semiconductors (OSCs), being light in weight, decomposable, cheap, and flexible, can be an excellent replacement for inorganic semiconductors. Reorganization energy (RE) is an essential parameter that can help determine charge carriers' mobility. Understanding and controlling the reorganization energy (RE) of OSCs is necessary for the design and optimization of organic electronic devices such as optoelectronic devices, organic photovoltaics, organic light-emitting diodes (OLEDs), photodetectors, and organic field-effect transistors (OFETs). The quantitative read-across structure-property relationship (q-RASPR) is an emerging computational methodology that can efficiently predict the material's property using structural and physiochemical features, and similarity measures. This method has also shown an enhancement in the predictivity of the models so developed. The stacking regression methodology uses the aggregates/predictions from the individual models and uses them according to their weights to produce an output in the new model. This study elaborates on the sequential steps for the development of a stacking q-RASPR model for the prediction of reorganization energy (RE) of the p-type organic semiconductors (OSCs) comprising 171 diverse organic structures of acenes, thiophenes, thienoacenes, and some pantalenes moiety as well. The predictions from the individual q-RASPR models developed using different similarity measures were used to perform the final stacking regression. Different machine learning (ML) algorithms were also used to enhance the model quality and predictivity. The model so developed through the stacking regression using support vector machine (SVM) method was of statistically good quality (R2 = 0.735), robustness (Q2LOO = 0.668), and effective predictive ability (Q2F1 = 0.801). The error in the model's predictions is also low compared to the other models developed earlier. This q-RASPR model was also used to predict an external set of 10888 compounds, and it shows an error (MAE) of only 87.95 meV, much lower than that reported earlier. The model developed in this study may be used to predict RE during high throughput screening of OSCs. [Display omitted]